National Health Surveys: Conducts large-scale surveys like NHMS to monitor Malaysia’s population health.
Public Health Research: Focuses on epidemiology, including non-communicable diseases, nutrition, communicable diseases, both among the general population and specific age groups.
Policy Support: Provides data-driven evidence to guide national health planning and interventions.
In describing a population, we often use a handful of samples rather than the whole population.
Unfortunately, sample distribution may differ from the population - gender, ethnicity, age.
Small studies typically limit their sample; clearly define the target population using inclusive and exclusive criteria.
But national surveys, including health surveys, require the sample to represent the general population (e.g., adult population, older person population, maternal and child population).
pacman::p_load(tidyverse, arrow)pyr_df <-read_parquet("https://storage.dosm.gov.my/population/population_malaysia.parquet") %>%filter(date ==as.Date("2025-01-01"), sex %in%c("male", "female"), age !="overall", ethnicity =="overall") %>%mutate(pop_k = population, pop =if_else(sex =="male", -pop_k, pop_k), age0 = readr::parse_number(age), age =fct_reorder(age, age0))ggplot(pyr_df, aes(x = age, y = pop, fill = sex)) +geom_col(width =0.9) +coord_flip() +scale_y_continuous(limits =c(-2000, 2000), breaks =seq(-2000, 2000, 500), labels =function(x) scales::comma(abs(x)), expand =expansion(mult =c(0.02, 0.02))) +labs(title ="Malaysia Population Pyramid, 2025", x ="Age group (years)", y ="Population (thousands)", fill ="Sex") +theme_minimal(base_size =13) +theme(panel.grid.minor =element_blank())
Complex Sampling
What is Complex Sampling?
Structured selection – Instead of simple random sampling, respondents are chosen through stratified and clustered sampling to ensure representation across diverse groups.
Unequal probabilities – Some groups are oversampled (e.g., small states, older adults) to obtain reliable estimates, necessitating the use of sampling weights to correct for these differences.
Design-based inference – Analysis must account for the survey’s design, including strata, clusters, and weights,so that standard errors and prevalence estimates accurately reflect the true population.
Why Complex Sampling?
Sampling: We use a sample to estimate the population efficiently, saving time, cost, and resources while still capturing key characteristics.
Stratification: Stratifying (by gender, ethnicity) ensures all important subgroups are represented and improves precision of estimates.
Clustering: Clustering respondents by area makes data collection logistically practical and cost-efficient.
Example - Diabetes among Malaysian (NHMS 2023)
Category
Overall %
95% CI
Male %
95% CI
Female %
95% CI
Malaysia
15.6
14.4–16.9
15.0
13.6–16.5
16.2
14.7–18.0
Age Group
18–29
3.2
2.2–4.6
3.7
2.2–6.1
2.6
1.7–4.1
30–39
6.5
5.2–8.1
6.9
5.0–9.3
6.0
4.5–7.9
40–49
15.2
13.2–17.4
13.7
11.1–16.8
16.8
14.2–19.8
50–59
28.8
25.0–33.0
28.4
24.2–33.0
29.3
24.4–34.7
60+
38.0
35.4–40.7
37.7
34.0–41.5
38.4
35.0–41.8
Ethnicity
Malay
16.2
15.1–17.4
15.5
14.1–17.1
16.9
15.4–18.4
Chinese
15.1
11.6–19.5
14.8
11.2–19.3
15.5
11.0–21.3
Indian
26.4
22.1–31.2
28.4
22.1–35.7
24.5
19.4–30.4
B. Sabah
9.3
7.3–11.8
9.5
6.8–13.0
9.1
6.5–12.6
B. Sarawak
17.2
13.0–22.3
14.9
10.4–21.0
19.3
14.3–25.6
Others
10.2
7.5–13.6
10.0
6.6–14.8
10.6
6.4–17.0
Simulation
Raw dataset need permission, so we will simulate here.
The simulated dataset can be obtain from github site.
